Evaluation of the natural history of cancer of the cervix, implications for prevention. The Cancer Risk Management Model (CRMM) – Human papillomavirus and cervical components

Abstract

a b s t r a c tThe Cancer Risk Management Model (CRMM) initiative of the Canadian Partnership Against Cancer offers policy makers a tool for making decisions regarding prevention and screening for their particular landscape.The cervical cancer component of CRMM is complex because the development of cervical cancer depends on HPV infection and has to take account of the fact that individuals must come in contact with one another for HPV to spread. Two tightly coupled models were built, one for the infectious spread of HPV (CRMM-HPV), and the other for the pathway from infection to disease onset, progression, screening, treatment, and mortality (CRMM-Cervical). This paper provides an overview of methods and functionality for CRMM components which simulate vaccination, screening, HPV incidence, disease progression, and cancer incidence.CRMM-HPV is a continuous-time, interacting-agent, Monte-Carlo microsimulation model that simulates sexual networks and HPV transmission. Six HPV groups (6, 11, 16, 18, other non-carcinogenic, other carcinogenic) and two vaccination types (bivalent, quadrivalent) were modeled. Input parameters include demography, sexual debut, partnership formation/separation and virus transmission, clearance, natural immunity. CRMM-HPV provides a 100-year projection of impacts of vaccinations on HPV infections. Results were scaled to reflect the Canadian population aged 10+ in 2011.Various vaccination scenarios can be compared by altering vaccination program design (target age, sex, program years, participation rate, vaccine type), vaccine efficacy, duration of protection and previous vaccination status. These parameters enable users to explore impacts of various scenarios such as targeting various age groups, adding boys, and booster and catch-up programs.

Effect of human papillomavirus vaccination on cervical cancer screening in Alberta

“…Optimal cervical cancer prevention requires both effective HPV vaccination and screening. 4 Screening may prevent cervical cancer for both unvaccinated and vaccinated women, considering that not all cancers and tumours are associated with the HPV strains included in the quadrivalent vaccine. 27,29 Thus, it is concerning that unvaccinated women are less likely to be screened ( Table 1).…”

“…1,2 Recently, HPV vaccination programs have been added to efforts to prevent cervical cancer. 3,4 Vaccination against HPV should reduce the prevalence of cervical dysplasia; thus, screening outcomes provide early evidence of the effectiveness of HPV vaccination against cancer. [5][6][7][8] Data are lacking on the association between Pap test results and HPV vaccination in the North American context.…”

Using the Cancer Risk Management Model to Evaluate the Health and Economic Impacts of Cytology Compared with Human Papillomavirus DNA Testing for Primary Cervical Cancer Screening in Canada

“…The cc and hpv transmission submodels were developed by a multidisciplinary team led by the Canadian Partnership Against Cancer in collaboration with Statistics Canada. The submodels are described in detail by Miller and colleagues 11 , with calibration and evaluation results additionally provided by Miller and colleagues 11 and by Evans and colleagues 15 .…”

“…In Europe, several countries start cc screening at later ages and use longer intervals than Canada does. For example, in Finland and the Netherlands, whose cc rates are comparable to those in Canada, cytology screening has been offered at 5-year intervals to women beginning at age 30 [11][12][13] . In 2013, the Canadian Task Force on Preventive Health Care (ctfphc) recommended routine screening for women 30-69 years of age, with weaker recommendations to routinely screen women 25-29 years of age.…”

The OncoSim Model: Development and Use for Better Decision-Making in Canadian Cancer Control

“…Additionally, peer-reviewed publication of model results, including comparisons with other modelling results, is encouraged from both internal and external users [2][3][4][5][6][7][8][9][10] . As newer data emerge, the model is used to compare past projections to current real-world data, thus continually assessing its ability to accurately project future events.…”